# Introduction to Probability Simulation and Gibbs Sampling with R

## byEric A. Suess, Bruce E. Trumbo

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The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation.No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.

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From the Publisher

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of d...

From the Jacket

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of d...

Eric A. Suess is Chair and Professor of Statistics and Biostatistics and Bruce E. Trumbo is Professor Emeritus of Statistics and Mathematics, both at California State University, East Bay. Professor Suess is experienced in applications of Bayesian methods and Gibbs sampling to epidemiology. Professor Trumbo is a fellow of the American ...
Format:PaperbackDimensions:320 pages, 9.25 × 6.1 × 0 inPublished:June 15, 2010Publisher:Springer New YorkLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:038740273X

ISBN - 13:9780387402734

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Table of Contents

Introductory Examples: Simulation, Estimation, and Graphics.- Generating Random Numbers.- Monte Carlo Integration and Limit Theorems.- Sampling from Applied Probability Models.- Screening Tests.- Markov Chains with Two States.- Examples of Markov Chains with Larger State Spaces.- Introduction to Bayesian Estimation.- Using Gibbs Samplers to Compute Bayesian Posterior Distributions.- Using WinBUGS for Bayesian Estimation.- Appendix: Getting Started with R.

Editorial Reviews

From the reviews:"Suess and Trumbo's book 'Introduction to Probability Simulation and Gibbs Sampling with R,' part of the 'Use R!' series, fits precisely into this framework of learning by doing-and doing again, with different distributions, or different parameters, or under different scenarios. . The book also contains an Appendix with an introduction to R, which should make it particularly attractive to students, who won't have to go to another source to learn about the basics. . an overall very useful book." (Nicole Lazar, Technometrics, Vol. 53 (3), August, 2011)